Пример #1
0
def prb_process(filepath):
    # hour based statistics
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.df.groupby('开始时间').mean()
    celled_df = cellkpi.df.groupby('小区名称').mean()
    overall_df = cellkpi.df.mean(numeric_only=True)

    print('hour based statistics processed successfully')

    # structure summary page for handover KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
        bh_df = cellkpi.df[cellkpi.df['Busy Hour'] == True].mean(numeric_only=True)
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['PRB KPI metrics'] = ['Mean DL PRB Utilization','Mean UL PRB Utilization']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [overall_df, bh_df]):
        dlPRB = nw_df.ix['下行Physical Resource Block被使用的平均个数 (无)']
        ulPRB = nw_df.ix['上行Physical Resource Block被使用的平均个数 (无)']
        summary_df[col] = [float(dlPRB),float(ulPRB)]
    pfm_dfs.append(summary_df)

    print('summary statistics precessed successfully')


    sheet_list = [pfm_dfs, timed_df, celled_df]
    name_list = ['performance overview', 'hour based', 'cell based']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #2
0
def csfb_process(filepath):
    # hour based statistics
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.timed_df

    timed_df = cellkpi.timed_df
    timed_df['CSFB成功率'] = timed_df['E-UTRAN向GERAN执行的CSFB重定向次数 (无)']/timed_df['eNodeB收到的CSFB触发次数 (无)']

    #wanted_cols = ['CQI总数', 'CQI总和', '平均CQI','RANK总数', 'RANK总和', '平均RANK']
    #timedoutput_df = pd.DataFrame(timed_df.ix[:, wanted_cols])
    print('hour based statistics precessed successfully')

    # structure summary page for DL Throughput KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['CSFB KPI metrics'] = ['CSFB attempt', 'CSFB execution','CSFB successful rate']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['CSFB成功率'] = nw_df['E-UTRAN向GERAN执行的CSFB重定向次数 (无)']/nw_df['eNodeB收到的CSFB触发次数 (无)']
        summary_df[col] = [nw_df['eNodeB收到的CSFB触发次数 (无)'],nw_df['E-UTRAN向GERAN执行的CSFB重定向次数 (无)'],
                           nw_df['CSFB成功率']]
    pfm_dfs.append(summary_df)



    print('summary statistics precessed successfully')

    # calculate Cell KPI
    cellkpi_df = cellkpi.celled_df.ix[:, 1:]
    cellkpi_df['CSFB成功率'] = cellkpi_df['E-UTRAN向GERAN执行的CSFB重定向次数 (无)']/cellkpi_df['eNodeB收到的CSFB触发次数 (无)']
    cellkpi_df['CSFB失败次数'] = cellkpi_df['eNodeB收到的CSFB触发次数 (无)']-cellkpi_df['E-UTRAN向GERAN执行的CSFB重定向次数 (无)']

    #wanted_cols = ['下行平均速率', '上行平均速率']

    #cellkpi_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    print('Cell KPI precessed successfully')

    # calculate Top List
    wanted_cols = ['CSFB失败次数','CSFB成功率','E-UTRAN向GERAN执行的CSFB重定向次数 (无)','eNodeB收到的CSFB触发次数 (无)']
    csfbfailTOP_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    csfbfailTOP_df = csfbfailTOP_df.sort_values(by='CSFB失败次数', ascending=False)


    print('TopN low Throughput precessed successfully')


    # write excel file
    sheet_list = [pfm_dfs, timed_df, cellkpi.celled_df, cellkpi_df, csfbfailTOP_df]
    name_list = ['performance overview', 'hour based', 'cell based', 'Cell KPI', 'CSFB Fail TOP List']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #3
0
def interference_process(filepath):
    # hour based statistics
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.timed_df

    timed_df = cellkpi.df.groupby('开始时间').mean()
    celled_df = cellkpi.df.groupby('小区名称').mean()
    overall_df = cellkpi.df.mean(numeric_only=True)

    print('hour based statistics precessed successfully')

    # structure summary page for handover KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
        bh_df = cellkpi.df[cellkpi.df['Busy Hour'] == True].mean(numeric_only=True)
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['UL Interference KPI metrics'] = ['Mean UL Interference']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [overall_df, bh_df]):
        mean_interference = nw_df.ix['系统上行每个PRB上检测到的干扰噪声的平均值 (毫瓦分贝)']
        summary_df[col] = [float(mean_interference)]
    pfm_dfs.append(summary_df)


    print('summary statistics precessed successfully')

    # calculate interference Top List
    wanted_cols = ['系统上行每个PRB上检测到的干扰噪声的平均值 (毫瓦分贝)']
    interference_df = pd.DataFrame(celled_df.ix[:, wanted_cols])
    interference_df = interference_df.sort_values(by='系统上行每个PRB上检测到的干扰噪声的平均值 (毫瓦分贝)', ascending=False)
    print('TopN UL interference precessed successfully')

    sheet_list = [pfm_dfs, timed_df, celled_df, interference_df]
    name_list = ['performance overview', 'hour based', 'cell based', 'TopN Interference']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #4
0
def tput_process(filepath):
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.timed_df

    # hour based statistics
    timed_df['下行平均速率'] = (timed_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)']/1048576)/(timed_df['小区PDCP发送下行数据的总时长 (毫秒)']/1000)
    timed_df['扣除小包后下行平均速率'] = ((timed_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)']-timed_df['使缓存为空的最后一个TTI所传的下行PDCP吞吐量 (比特)'])/1048576) \
                                        /(timed_df['扣除使下行缓存为空的最后一个TTI之后的数传时长 (毫秒)']/1000)

    timed_df['上行平均速率'] = (timed_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)']/1048576)/(timed_df['小区PDCP接收上行数据的总时长 (毫秒)']/1000)
    timed_df['扣除小包后上行平均速率'] = ((timed_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)']-timed_df['使UE缓存为空的最后一个TTI所传的上行PDCP吞吐量 (比特)'])/1048576) \
                                        /(timed_df['扣除使UE缓存为空的最后一个TTI之后的上行数传时长 (毫秒)']/1000)
    print('hour based statistics precessed successfully')

    # structure summary page for DL Throughput KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['DL Throughput KPI metrics'] = ['Total Throughput', 'Total Scheduling Time','Small packet removed Throughput',
                                            'Small packet removed Scheduling Time', 'Average DL Throughput',
                                            'Average DL Throughput(Small packet removed)']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['下行扣除小包'] = nw_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)']-nw_df['使缓存为空的最后一个TTI所传的下行PDCP吞吐量 (比特)']
        avg_dl_tput = float((nw_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)']/1048576)/(nw_df['小区PDCP发送下行数据的总时长 (毫秒)']/1024))
        avg_dl_tput2 = float((nw_df['下行扣除小包']/1048576)/(nw_df['扣除使下行缓存为空的最后一个TTI之后的数传时长 (毫秒)']/1024))

        summary_df[col] = [nw_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)'],nw_df['小区PDCP发送下行数据的总时长 (毫秒)'],
                           nw_df['下行扣除小包'],nw_df['扣除使下行缓存为空的最后一个TTI之后的数传时长 (毫秒)'],
                           avg_dl_tput,avg_dl_tput2]
    pfm_dfs.append(summary_df)

    #UL Throughput KPI
    summary_df = pd.DataFrame()
    summary_df['UL Throughput KPI metrics'] = ['Total Throughput', 'Total Scheduling Time','Small packet removed Throughput',
                                            'Small packet removed Scheduling Time', 'Average UL Throughput',
                                            'Average UL Throughput(Small packet removed)']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['上行扣除小包'] = nw_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)']-nw_df['使UE缓存为空的最后一个TTI所传的上行PDCP吞吐量 (比特)']
        avg_ul_tput = float((nw_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)']/1048576)/(nw_df['小区PDCP接收上行数据的总时长 (毫秒)']/1024))
        avg_ul_tput2 = float((nw_df['上行扣除小包']/1048576)/(nw_df['扣除使UE缓存为空的最后一个TTI之后的上行数传时长 (毫秒)']/1024))

        summary_df[col] = [nw_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)'],nw_df['小区PDCP接收上行数据的总时长 (毫秒)'],
                           nw_df['上行扣除小包'],nw_df['扣除使UE缓存为空的最后一个TTI之后的上行数传时长 (毫秒)'],
                           avg_ul_tput,avg_ul_tput2]

    pfm_dfs.append(summary_df)

    print('summary statistics precessed successfully')

    # calculate Cell KPI
    cellkpi_df = cellkpi.celled_df.ix[:, 1:]
    cellkpi_df['下行平均速率'] = (cellkpi_df['小区PDCP层所发送的下行数据的总吞吐量 (比特)']/1048576)/(cellkpi_df['小区PDCP发送下行数据的总时长 (毫秒)']/1000)
    cellkpi_df['上行平均速率'] = (cellkpi_df['小区PDCP层所接收到的上行数据的总吞吐量 (比特)']/1048576)/(cellkpi_df['小区PDCP接收上行数据的总时长 (毫秒)']/1000)

    wanted_cols = ['下行平均速率', '上行平均速率']

    cellkpi_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    print('Cell KPI precessed successfully')

    # calculate 2* Top List
    wanted_cols = ['下行平均速率']
    dl_top_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    dl_top_df = dl_top_df.sort_values(by='下行平均速率', ascending=True)
    wanted_cols = ['上行平均速率']
    ul_top_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    ul_top_df = ul_top_df.sort_values(by='上行平均速率', ascending=True)

    print('TopN low Throughput precessed successfully')


    # write excel file
    sheet_list = [pfm_dfs, timed_df, cellkpi.celled_df, cellkpi_df, dl_top_df,ul_top_df]
    name_list = ['performance overview', 'hour based', 'cell based', 'Cell KPI', 'DL Low Throughput TOP List','UL Low Throughput TOP List']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #5
0
def ho_process(filepath):
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.timed_df
    timed_df['切换准备请求'] = timed_df['eNodeB间异频切换出尝试次数 (无)']+timed_df['eNodeB间同频切换出尝试次数 (无)']+\
                         timed_df['eNodeB内同频切换出尝试次数 (无)']+timed_df['eNodeB内异频切换出尝试次数 (无)']
    timed_df['切换执行请求'] = timed_df['eNodeB间同频切换出执行次数 (无)']+timed_df['eNodeB间异频切换出执行次数 (无)']+\
                         timed_df['eNodeB内同频切换出执行次数 (无)']+timed_df['eNodeB内异频切换出执行次数 (无)']
    timed_df['切换执行成功'] = timed_df['eNodeB间异频切换出成功次数 (无)']+timed_df['eNodeB间同频切换出成功次数 (无)']+\
                         timed_df['eNodeB内同频切换出成功次数 (无)']+timed_df['eNodeB内异频切换出成功次数 (无)']-\
                         timed_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']-\
                         timed_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)']-\
                         timed_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)']-\
                         timed_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']
    timed_df['Handover Successful Rate'] = timed_df['切换执行成功']/timed_df['切换准备请求']
    timed_df['Preparation Successful Rate'] = timed_df['切换执行请求']/timed_df['切换准备请求']
    timed_df['Execution Successful Rate'] = timed_df['切换执行成功']/timed_df['切换执行请求']
    wanted_cols = ['Handover Successful Rate', 'Preparation Successful Rate', 'Execution Successful Rate']
    timedoutput_df = pd.DataFrame(timed_df.ix[:, wanted_cols])
    print('hour based statistics precessed successfully')

    # structure summary page for handover KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()

    pfm_dfs = list()
    summary_df = pd.DataFrame()
    summary_df['Handover KPI metrics'] = ['Preparation attempt', 'Preparation success/Execution attempt',
                                          'Execution success', 'Handover Successful Rate',
                                          'Preparation Successful Rate', 'Execution Successful Rate']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        prep_att = nw_df.ix['eNodeB间异频切换出尝试次数 (无)']+nw_df.ix['eNodeB间同频切换出尝试次数 (无)']+nw_df.ix['eNodeB内同频切换出尝试次数 (无)']+nw_df.ix['eNodeB内异频切换出尝试次数 (无)']
        prep_succ = nw_df.ix['eNodeB间同频切换出执行次数 (无)']+nw_df.ix['eNodeB间异频切换出执行次数 (无)']+nw_df.ix['eNodeB内同频切换出执行次数 (无)']+nw_df.ix['eNodeB内异频切换出执行次数 (无)']
        exec_succ = nw_df.ix['eNodeB间异频切换出成功次数 (无)']+nw_df.ix['eNodeB间同频切换出成功次数 (无)']+nw_df.ix['eNodeB内同频切换出成功次数 (无)']+nw_df.ix['eNodeB内异频切换出成功次数 (无)']-nw_df.ix['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']-nw_df.ix['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)']-nw_df.ix['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)']-nw_df.ix['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']
        ho_sr = float(exec_succ/prep_att*100.0)
        prep_sr = float(prep_succ/prep_att*100.0)
        exec_sr = float(exec_succ/prep_succ*100.0)
        summary_df[col] = [int(prep_att), int(prep_succ), int(exec_succ), ho_sr, prep_sr, exec_sr]
    pfm_dfs.append(summary_df)

    summary_df = pd.DataFrame()
    summary_df['Handover preparation metrics'] = ['Total Preparation Failure', 'X2 Based  Preparation Failure',
                                                  'S1 based  Preparation Failure',
                                                  'Intra-eNodeB  Preparation Failure']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        total_prep_fail = nw_df['eNodeB间异频切换出尝试次数 (无)']+nw_df['eNodeB间同频切换出尝试次数 (无)']+\
                          nw_df['eNodeB内同频切换出尝试次数 (无)']+nw_df['eNodeB内异频切换出尝试次数 (无)']-\
                          nw_df['eNodeB间同频切换出执行次数 (无)']-nw_df['eNodeB间异频切换出执行次数 (无)']-\
                          nw_df['eNodeB内同频切换出执行次数 (无)']-nw_df['eNodeB内异频切换出执行次数 (无)']
        x2_prep_fail = nw_df['eNodeB间X2接口同频切换出尝试次数 (无)']-nw_df['eNodeB间X2接口同频切换出执行次数 (无)']+\
                     nw_df['eNodeB间X2接口异频切换出尝试次数 (无)']-nw_df['eNodeB间X2接口异频切换出执行次数 (无)']
        intraeNB_fail=nw_df['eNodeB内同频切换出尝试次数 (无)']+nw_df['eNodeB内异频切换出尝试次数 (无)']-\
                      nw_df['eNodeB内同频切换出执行次数 (无)']-nw_df['eNodeB内异频切换出执行次数 (无)']
        s1_prep_fail = total_prep_fail - x2_prep_fail - intraeNB_fail
        summary_df[col] = [int(total_prep_fail), int(x2_prep_fail), int(s1_prep_fail), int(intraeNB_fail)]
    pfm_dfs.append(summary_df)

    # HO Preparation failure breakdown
    summary_df = pd.DataFrame()
    summary_df ['Handover Preparation Failure Breakdown'] = ['MME reason', 'Target cell no reply',
                                                  'Target cell preparation failure',
                                                  'HO cancel','Target cell response message illegal','other reason']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        summary_df[col] = [nw_df['核心网原因导致模式内切换出准备失败次数 (无)'], nw_df['目标小区无响应导致模式内切换出准备失败次数 (无)'],nw_df['目标小区回复切换准备失败消息导致模式内切换出准备失败次数 (无)'],nw_df['源小区发送切换取消导致模式内切换出准备失败次数 (无)'],nw_df['对端回复切换响应消息合法性检查失败导致切换出准备失败次数 (无)'],nw_df['其它原因导致小区模式内切换出准备失败次数 (无)']]
    pfm_dfs.append(summary_df)


    summary_df = pd.DataFrame()
    summary_df['Handover execution metrics'] = ['Total Handover Execution Fail', 'Handover Execution Fail',
                                                  ' Handover Execution Success by Re-establishment']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        total_exec_fail = nw_df['eNodeB间同频切换出执行次数 (无)']+nw_df['eNodeB间异频切换出执行次数 (无)'] +\
                        nw_df['eNodeB内同频切换出执行次数 (无)']+nw_df['eNodeB内异频切换出执行次数 (无)'] -\
                        nw_df['eNodeB间异频切换出成功次数 (无)']-nw_df['eNodeB间同频切换出成功次数 (无)'] -\
                        nw_df['eNodeB内同频切换出成功次数 (无)']-nw_df['eNodeB内异频切换出成功次数 (无)'] +\
                        nw_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)'] +\
                        nw_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)'] +\
                        nw_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)'] +\
                        nw_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']
        ho_exec_fail = nw_df['eNodeB间同频切换出执行次数 (无)']+nw_df['eNodeB间异频切换出执行次数 (无)'] +\
                     nw_df['eNodeB内同频切换出执行次数 (无)']+nw_df['eNodeB内异频切换出执行次数 (无)'] -\
                     nw_df['eNodeB间异频切换出成功次数 (无)']-nw_df['eNodeB间同频切换出成功次数 (无)'] -\
                     nw_df['eNodeB内同频切换出成功次数 (无)']-nw_df['eNodeB内异频切换出成功次数 (无)'] +\
                     nw_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']
        ho_exec_succ_reest = nw_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)'] +\
                           nw_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)'] +\
                           nw_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)'] +\
                           nw_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']
        summary_df[col] = [int(total_exec_fail), int(ho_exec_fail), int(ho_exec_succ_reest)]
    pfm_dfs.append(summary_df)

    summary_df = pd.DataFrame()
    summary_df['Intra/Inter Frequency Handover execution metrics'] = ['Execution Failure', 'Execution Attempt',
                                                'Execution Successful Rate']
    nw_df = cellkpi.overall_df
    # intra-frequency
    exec_fail = nw_df['eNodeB间同频切换出执行次数 (无)']+nw_df['eNodeB内同频切换出执行次数 (无)']-nw_df['eNodeB间同频切换出成功次数 (无)']-nw_df['eNodeB内同频切换出成功次数 (无)']+nw_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']+nw_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)']
    exec_att = nw_df['eNodeB间同频切换出执行次数 (无)']+nw_df['eNodeB内同频切换出执行次数 (无)']
    exec_sr = float(100.0 - (exec_fail/exec_att)*100.0)
    summary_df['Intra frequency handover execution'] = [int(exec_fail), int(exec_att), exec_sr]
    # inter-frequency
    exec_fail = nw_df['eNodeB间异频切换出执行次数 (无)']+nw_df['eNodeB内异频切换出执行次数 (无)']-nw_df['eNodeB间异频切换出成功次数 (无)']-nw_df['eNodeB内异频切换出成功次数 (无)']+nw_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']+nw_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)']
    exec_att = nw_df['eNodeB间异频切换出执行次数 (无)']+nw_df['eNodeB内异频切换出执行次数 (无)']
    exec_sr = float(100.0 - (exec_fail/exec_att)*100.0)
    summary_df['Inter frequency handover execution'] = [int(exec_fail), int(exec_att), exec_sr]
    pfm_dfs.append(summary_df)

    print('summary statistics precessed successfully')

    # calculate Cell KPI
    cellkpi_df = cellkpi.celled_df.ix[:, 1:]
    cellkpi_df['切换准备请求'] = cellkpi_df['eNodeB间异频切换出尝试次数 (无)']+cellkpi_df['eNodeB间同频切换出尝试次数 (无)']+cellkpi_df['eNodeB内同频切换出尝试次数 (无)']+cellkpi_df['eNodeB内异频切换出尝试次数 (无)']
    cellkpi_df['切换执行请求'] = cellkpi_df['eNodeB间同频切换出执行次数 (无)']+cellkpi_df['eNodeB间异频切换出执行次数 (无)']+cellkpi_df['eNodeB内同频切换出执行次数 (无)']+cellkpi_df['eNodeB内异频切换出执行次数 (无)']
    cellkpi_df['切换执行成功'] = cellkpi_df['eNodeB间异频切换出成功次数 (无)']+cellkpi_df['eNodeB间同频切换出成功次数 (无)']+cellkpi_df['eNodeB内同频切换出成功次数 (无)']+cellkpi_df['eNodeB内异频切换出成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)']
    cellkpi_df['切换准备失败'] = cellkpi_df['切换准备请求']-cellkpi_df['切换执行请求']
    cellkpi_df['切换执行失败'] = cellkpi_df['切换执行请求']-cellkpi_df['切换执行成功']
    cellkpi_df['切换成功率'] = cellkpi_df['切换执行成功']/cellkpi_df['切换准备请求']*100.0
    cellkpi_df['切换准备成功率'] = cellkpi_df['切换执行请求']/cellkpi_df['切换准备请求']*100.0
    cellkpi_df['切换执行成功率'] = cellkpi_df['切换执行成功']/cellkpi_df['切换执行请求']*100.0
    cellkpi_df['同频切换执行成功率'] = (cellkpi_df['eNodeB间同频切换出成功次数 (无)']+cellkpi_df['eNodeB内同频切换出成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB内同频切换出执行成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB间同频切换出执行成功次数 (无)'])/(cellkpi_df['eNodeB间同频切换出执行次数 (无)']+cellkpi_df['eNodeB内同频切换出执行次数 (无)'])*100.0
    cellkpi_df['异频切换执行成功率'] = (cellkpi_df['eNodeB间异频切换出成功次数 (无)']+cellkpi_df['eNodeB内异频切换出成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB间异频切换出执行成功次数 (无)']-cellkpi_df['通过重建回源小区的eNodeB内异频切换出执行成功次数 (无)'])/(cellkpi_df['eNodeB间异频切换出执行次数 (无)']+cellkpi_df['eNodeB内异频切换出执行次数 (无)'])*100.0
    wanted_cols = ['切换准备请求', '切换执行请求', '切换执行成功', '切换准备失败', '切换执行失败', '切换成功率', '切换准备成功率', '切换执行成功率', '同频切换执行成功率', '异频切换执行成功率','无对应的邻区关系导致无法发起同频切换过程的次数 (无)', '无对应的邻区关系导致无法发起异频切换过程的次数 (无)']
    cellkpi_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    print('Cell KPI precessed successfully')

    # calculate Handover preparation failure Top List
    wanted_cols = ['切换准备失败', '切换准备请求', '切换执行请求', '切换准备成功率']
    hoprepfail_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    hoprepfail_df = hoprepfail_df.sort_values(by='切换准备失败', ascending=False)
    print('TopN HO Prep precessed successfully')

    # calculate Handover execution failure Top List
    wanted_cols = ['切换执行失败', '切换执行请求', '切换执行成功', '切换执行成功率', '同频切换执行成功率','异频切换执行成功率','无对应的邻区关系导致无法发起同频切换过程的次数 (无)', '无对应的邻区关系导致无法发起异频切换过程的次数 (无)']
    hoexecfail_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    hoexecfail_df = hoexecfail_df.sort_values(by='切换执行失败', ascending=False)
    print('TopN HO Exec precessed successfully')

    # write excel file
    sheet_list = [pfm_dfs, timedoutput_df, cellkpi.celled_df, cellkpi_df, hoprepfail_df, hoexecfail_df]
    name_list = ['performance overview', 'hour based', 'cell based', 'Cell KPI', 'TopN HO Prep', 'TopN HO Exec']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #6
0
def erab_process(filepath):
    # hour based statistics
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    timed_df = cellkpi.timed_df


    cellkpi.overall_df['E-RAB建立平均时长 (毫秒)'] = cellkpi.df['E-RAB建立平均时长 (毫秒)'].mean()
    timed_df['ERAB建立成功率'] = timed_df['E-RAB建立成功总次数 (无)']/timed_df['E-RAB建立尝试总次数 (无)']*100
    timed_df['E-RAB建立平均时长 (毫秒)'] = cellkpi.df.groupby('开始时间')['E-RAB建立平均时长 (毫秒)'].mean()
    wanted_cols = ['E-RAB建立尝试总次数 (无)', 'E-RAB建立成功总次数 (无)','ERAB建立成功率',
                   'E-RAB建立平均时长 (毫秒)']
    timedoutput_df = pd.DataFrame(timed_df.ix[:, wanted_cols])
    print('hour based statistics precessed successfully')

    # structure summary page for ERAB KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
        cellkpi.bh_df['E-RAB建立平均时长 (毫秒)'] = cellkpi.df[cellkpi.df['Busy Hour'] == True]['E-RAB建立平均时长 (毫秒)'].mean()
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['ERAB setup successful rate KPI metrics'] = ['L.E-RAB.AttEst', 'L.E-RAB.SuccEst','ERAB setup successful rate',
                                                            'L.E-RAB.Est.TimeAvg']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['ERAB建立成功率'] = nw_df['E-RAB建立成功总次数 (无)']/nw_df['E-RAB建立尝试总次数 (无)']*100
        summary_df[col] = [nw_df['E-RAB建立尝试总次数 (无)'],nw_df['E-RAB建立成功总次数 (无)'],
                           nw_df['ERAB建立成功率'],nw_df['E-RAB建立平均时长 (毫秒)']]
    pfm_dfs.append(summary_df)

    #erab procedure failure reason breakdown
    summary_df = pd.DataFrame()
    summary_df['ERAB Setup Procedure Failure Reason Breakdown'] = ['Total Failure', 'L.E-RAB.FailEst.MME',
                                                                   'L.E-RAB.FailEst.TNL','L.E-RAB.FailEst.RNL','L.E-RAB.FailEst.Other']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        total_failure = nw_df['E-RAB建立尝试总次数 (无)']-nw_df['E-RAB建立成功总次数 (无)']
        summary_df[col] = [total_failure,nw_df['核心网问题导致E-RAB建立失败次数 (无)'],nw_df['传输层问题导致E-RAB建立失败次数 (无)'],
                            nw_df['无线层问题导致E-RAB建立失败次数 (无)'],nw_df['其它原因导致E-RAB建立失败次数 (无)']]
    pfm_dfs.append(summary_df)

    #ERAB Setup Failure TNL Reason Breakdown
    summary_df = pd.DataFrame()
    summary_df['ERAB Setup Failure TNL Reason Breakdown'] = ['L.E-RAB.FailEst.TNL.DLRes', 'L.E-RAB.FailEst.TNL.ULRes',
                                                             'L.E-RAB.FailEst.TNL.Other']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        summary_df[col] = [nw_df['下行传输资源不足导致E-RAB建立失败次数 (无)'],
                           nw_df['上行传输资源不足导致E-RAB建立失败次数 (无)'],
                           nw_df['其它传输层问题导致E-RAB建立失败次数 (无)']]
    pfm_dfs.append(summary_df)

    #ERAB Setup Failure RNL Reason Breakdown
    summary_df = pd.DataFrame()
    summary_df['ERAB Setup Failure RNL Reason Breakdown'] = ['L.E-RAB.FailEst.SecurModeFail', 'L.E-RAB.FailEst.NoReply',
                                                             'L.E-RAB.FailEst.SRBReset','L.E-RAB.FailEst.NoRadioRes',
                                                             'L.E-RAB.FailEst.Conflict.Hofail','L.E-RAB.FailEst.RNL.Other']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        summary_df[col] = [nw_df['安全模式配置失败导致E-RAB建立失败次数 (无)'],
                           nw_df['等待UE响应超时导致E-RAB建立失败次数 (无)'],
                           nw_df['SRB RLC达到最大重传次数导致E-RAB建立失败次数 (无)'],
                           nw_df['无线资源不足导致E-RAB建立失败次数 (无)'],
                           nw_df['与站内切换冲突导致E-RAB建立失败次数 (无)'],
                           nw_df['其它无线资源不足导致E-RAB建立失败次数 (无)']]
    pfm_dfs.append(summary_df)

    print('summary statistics precessed successfully')

    # calculate Cell KPI
    cellkpi_df = cellkpi.celled_df.ix[:, 1:]
    cellkpi_df['ERAB建立失败次数'] = cellkpi_df['E-RAB建立尝试总次数 (无)']-cellkpi_df['E-RAB建立成功总次数 (无)']
    cellkpi_df['ERAB建立成功率'] = cellkpi_df['E-RAB建立成功总次数 (无)']/cellkpi_df['E-RAB建立尝试总次数 (无)']*100
    wanted_cols = ['ERAB建立失败次数', 'ERAB建立成功率','E-RAB建立尝试总次数 (无)','E-RAB建立成功总次数 (无)',
                   '核心网问题导致E-RAB建立失败次数 (无)','传输层问题导致E-RAB建立失败次数 (无)',
                   '无线层问题导致E-RAB建立失败次数 (无)','其它原因导致E-RAB建立失败次数 (无)',
                   '下行传输资源不足导致E-RAB建立失败次数 (无)',
                   '上行传输资源不足导致E-RAB建立失败次数 (无)',
                   '其它传输层问题导致E-RAB建立失败次数 (无)',
                   '安全模式配置失败导致E-RAB建立失败次数 (无)',
                   '等待UE响应超时导致E-RAB建立失败次数 (无)',
                   'SRB RLC达到最大重传次数导致E-RAB建立失败次数 (无)',
                   '无线资源不足导致E-RAB建立失败次数 (无)',
                   '与站内切换冲突导致E-RAB建立失败次数 (无)',
                   '其它无线资源不足导致E-RAB建立失败次数 (无)']
    cellkpi_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    print('Cell KPI precessed successfully')

    # calculate failure Top List
    wanted_cols = ['ERAB建立失败次数', 'ERAB建立成功率','E-RAB建立尝试总次数 (无)','E-RAB建立成功总次数 (无)',
                   '核心网问题导致E-RAB建立失败次数 (无)','传输层问题导致E-RAB建立失败次数 (无)',
                   '无线层问题导致E-RAB建立失败次数 (无)','其它原因导致E-RAB建立失败次数 (无)',
                   '下行传输资源不足导致E-RAB建立失败次数 (无)',
                   '上行传输资源不足导致E-RAB建立失败次数 (无)',
                   '其它传输层问题导致E-RAB建立失败次数 (无)',
                   '安全模式配置失败导致E-RAB建立失败次数 (无)',
                   '等待UE响应超时导致E-RAB建立失败次数 (无)',
                   'SRB RLC达到最大重传次数导致E-RAB建立失败次数 (无)',
                   '无线资源不足导致E-RAB建立失败次数 (无)',
                   '与站内切换冲突导致E-RAB建立失败次数 (无)',
                   '其它无线资源不足导致E-RAB建立失败次数 (无)']
    erabTOP_df = pd.DataFrame(cellkpi_df.ix[:, wanted_cols])
    erabTOP_df = erabTOP_df.sort_values(by='ERAB建立失败次数', ascending=False)

    print('TopN Radio Drop precessed successfully')


    # write excel file
    sheet_list = [pfm_dfs, timedoutput_df, cellkpi.celled_df, cellkpi_df, erabTOP_df]
    name_list = ['performance overview', 'hour based', 'cell based', 'Cell KPI',
                 'earb setup fail TOP List']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')
Пример #7
0
def mcs_process(filepath):
    cellkpi = CellKPI(filepath)
    if not cellkpi.readfile(', skiprows=7, na_values=["NIL"]'):
        print('Read Excel file failed')
        return False

    # hour based statistics
    timed_df = cellkpi.timed_df

    print('hour based statistics precessed successfully')

    # structure summary page for CQI RI KPI
    if not hasattr(cellkpi, 'bh_df'):
        cellkpi.busyhour()
    pfm_dfs = list()

    summary_df = pd.DataFrame()
    summary_df['DL MCS KPI metrics'] = ['MCS=0 Count', 'MCS=1 Count','MCS=2 Count','MCS=3 Count','MCS=4 Count','MCS=5 Count',
                                        'MCS=6 Count','MCS=7 Count', 'MCS=8 Count','MCS=9 Count','MCS=10 Count','MCS=11 Count',
                                        'MCS=12 Count','MCS=13 Count','MCS=14 Count','MCS=15 Count','MCS=16 Count','MCS=17 Count',
                                        'MCS=18 Count','MCS=19 Count','MCS=20 Count','MCS=21 Count','MCS=22 Count','MCS=23 Count',
                                        'MCS=24 Count','MCS=25 Count','MCS=26 Count','MCS=27 Count','MCS=28 Count','MCS=29 Count',
                                        'MCS=30 Count','MCS=31 Count',
                                        'Avg MCS','Avg MCS (0&1 excluded)']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['MeanMCS1'] = 0
        nw_df['MCS Count1'] = 0
        for i in range(28):
            nw_df['MeanMCS1'] += nw_df['对PDSCH进行调度时选择了MCS index %d的次数 (无)' %i] * i
            nw_df['MCS Count1'] += nw_df['对PDSCH进行调度时选择了MCS index %d的次数 (无)' %i]
        nw_df['MeanMCS1'] = nw_df['MeanMCS1']/nw_df['MCS Count1']

        nw_df['MeanMCS2'] = 0
        nw_df['MCS Count2'] = 0
        for i in range(2, 28):
            nw_df['MeanMCS2'] += nw_df['对PDSCH进行调度时选择了MCS index %d的次数 (无)' %i] * i
            nw_df['MCS Count2'] += nw_df['对PDSCH进行调度时选择了MCS index %d的次数 (无)' %i]
        nw_df['MeanMCS2'] = nw_df['MeanMCS2']/nw_df['MCS Count2']

        summary_df[col] = [nw_df['对PDSCH进行调度时选择了MCS index %d的次数 (无)' %x] for x in range(32)] +  [nw_df['MeanMCS1'], nw_df['MeanMCS2']]
    pfm_dfs.append(summary_df)

    #UL MCS summary
    summary_df = pd.DataFrame()
    summary_df['UL MCS KPI metrics'] = ['MCS=0 Count', 'MCS=1 Count','MCS=2 Count','MCS=3 Count','MCS=4 Count','MCS=5 Count',
                                        'MCS=6 Count','MCS=7 Count', 'MCS=8 Count','MCS=9 Count','MCS=10 Count','MCS=11 Count',
                                        'MCS=12 Count','MCS=13 Count','MCS=14 Count','MCS=15 Count','MCS=16 Count','MCS=17 Count',
                                        'MCS=18 Count','MCS=19 Count','MCS=20 Count','MCS=21 Count','MCS=22 Count','MCS=23 Count',
                                        'MCS=24 Count','MCS=25 Count','MCS=26 Count','MCS=27 Count','MCS=28 Count','MCS=29 Count',
                                        'MCS=30 Count','MCS=31 Count',
                                        'Avg MCS']
    for col, nw_df in zip(['Overall', 'Busy Hour'], [cellkpi.overall_df, cellkpi.bh_df]):
        nw_df['MeanULMCS'] = (nw_df['对PUSCH进行调度时选择了MCS index 1的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 2的次数 (无)']*2+\
                             nw_df['对PUSCH进行调度时选择了MCS index 3的次数 (无)']*3+nw_df['对PUSCH进行调度时选择了MCS index 4的次数 (无)']*4+\
                             nw_df['对PUSCH进行调度时选择了MCS index 5的次数 (无)']*5+nw_df['对PUSCH进行调度时选择了MCS index 6的次数 (无)']*6+\
                             nw_df['对PUSCH进行调度时选择了MCS index 7的次数 (无)']*7+nw_df['对PUSCH进行调度时选择了MCS index 8的次数 (无)']*8+\
                             nw_df['对PUSCH进行调度时选择了MCS index 9的次数 (无)']*9+nw_df['对PUSCH进行调度时选择了MCS index 10的次数 (无)']*10+\
                             nw_df['对PUSCH进行调度时选择了MCS index 11的次数 (无)']*11+nw_df['对PUSCH进行调度时选择了MCS index 12的次数 (无)']*12+\
                             nw_df['对PUSCH进行调度时选择了MCS index 13的次数 (无)']*13+nw_df['对PUSCH进行调度时选择了MCS index 14的次数 (无)']*14+\
                             nw_df['对PUSCH进行调度时选择了MCS index 15的次数 (无)']*15+nw_df['对PUSCH进行调度时选择了MCS index 16的次数 (无)']*16+\
                             nw_df['对PUSCH进行调度时选择了MCS index 17的次数 (无)']*17+nw_df['对PUSCH进行调度时选择了MCS index 18的次数 (无)']*18+\
                             nw_df['对PUSCH进行调度时选择了MCS index 19的次数 (无)']*19+nw_df['对PUSCH进行调度时选择了MCS index 20的次数 (无)']*20+\
                             nw_df['对PUSCH进行调度时选择了MCS index 21的次数 (无)']*21+nw_df['对PUSCH进行调度时选择了MCS index 22的次数 (无)']*22+\
                             nw_df['对PUSCH进行调度时选择了MCS index 23的次数 (无)']*23+nw_df['对PUSCH进行调度时选择了MCS index 24的次数 (无)']*24+\
                             nw_df['对PUSCH进行调度时选择了MCS index 25的次数 (无)']*25+nw_df['对PUSCH进行调度时选择了MCS index 26的次数 (无)']*26+\
                             nw_df['对PUSCH进行调度时选择了MCS index 27的次数 (无)']*27+nw_df['对PUSCH进行调度时选择了MCS index 28的次数 (无)']*28)\
                             /(nw_df['对PUSCH进行调度时选择了MCS index 0的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 1的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 2的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 3的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 4的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 5的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 6的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 7的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 8的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 9的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 10的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 11的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 12的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 13的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 14的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 15的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 16的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 17的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 18的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 19的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 20的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 21的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 22的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 23的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 24的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 25的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 26的次数 (无)']+\
                             nw_df['对PUSCH进行调度时选择了MCS index 27的次数 (无)']+nw_df['对PUSCH进行调度时选择了MCS index 28的次数 (无)'])


        summary_df[col] = [nw_df['对PUSCH进行调度时选择了MCS index 0的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 1的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 2的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 3的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 4的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 5的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 6的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 7的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 8的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 9的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 10的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 11的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 12的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 13的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 14的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 15的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 16的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 17的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 18的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 19的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 20的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 21的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 22的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 23的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 24的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 25的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 26的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 27的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 28的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 29的次数 (无)'],nw_df['对PUSCH进行调度时选择了MCS index 30的次数 (无)'],\
                             nw_df['对PUSCH进行调度时选择了MCS index 31的次数 (无)'],nw_df['MeanULMCS']]
    pfm_dfs.append(summary_df)

    print('summary statistics precessed successfully')

    # write excel file
    sheet_list = [pfm_dfs, timed_df, cellkpi.celled_df]
    name_list = ['performance overview', 'hour based', 'cell based']
    cellkpi.writefile(sheet_list, name_list)
    print('write Excel file successfully')